Use raw fastq and generate the quality plots to asses the quality of reads
Filter and trim out bad sequences and bases from our sequencing files
Write out fastq files with high quality sequences
Evaluate the quality from our filter and trim.
Infer errors on forward and reverse reads individually
Identified ASVs on forward and reverse reads separately using the error model.
Merge forward and reverse ASVs into “contigous ASVs”.
Generate ASV count table. (otu_table input for
phyloseq.).
ASV count table: otu_table
Taxonomy table tax_table
Sample information: sample_table track the reads
lost throughout DADA2 workflow.
#Set the raw fastq path to the raw sequencing files
#Path to the fastq files
raw_fastqs_path <- "data/01_DADA2/01_raw_gzipped_fastqs"
#What files are in this path (Intuition check)
list.files(raw_fastqs_path)## [1] "568_4_S245_L001_R1_001.fastq.gz" "568_4_S245_L001_R2_001.fastq.gz"
## [3] "568_5_S246_L001_R1_001.fastq.gz" "568_5_S246_L001_R2_001.fastq.gz"
## [5] "581_5_S247_L001_R1_001.fastq.gz" "581_5_S247_L001_R2_001.fastq.gz"
## [7] "611_5_S248_L001_R1_001.fastq.gz" "611_5_S248_L001_R2_001.fastq.gz"
## [9] "E03_5_S146_L001_R1_001.fastq.gz" "E03_5_S146_L001_R2_001.fastq.gz"
## [11] "E05_5_S147_L001_R1_001.fastq.gz" "E05_5_S147_L001_R2_001.fastq.gz"
## [13] "E1_4_S140_L001_R1_001.fastq.gz" "E1_4_S140_L001_R2_001.fastq.gz"
## [15] "E1_5_S141_L001_R1_001.fastq.gz" "E1_5_S141_L001_R2_001.fastq.gz"
## [17] "E2_4_S142_L001_R1_001.fastq.gz" "E2_4_S142_L001_R2_001.fastq.gz"
## [19] "E2_5_S143_L001_R1_001.fastq.gz" "E2_5_S143_L001_R2_001.fastq.gz"
## [21] "E3_4_S144_L001_R1_001.fastq.gz" "E3_4_S144_L001_R2_001.fastq.gz"
## [23] "E3_5_S145_L001_R1_001.fastq.gz" "E3_5_S145_L001_R2_001.fastq.gz"
## [25] "Neg1_S148_L001_R1_001.fastq.gz" "Neg1_S148_L001_R2_001.fastq.gz"
## [27] "Neg2_S249_L001_R1_001.fastq.gz" "Neg2_S249_L001_R2_001.fastq.gz"
## chr [1:28] "568_4_S245_L001_R1_001.fastq.gz" ...
#Create a vector of forward reads
forward_reads <- list.files(raw_fastqs_path, pattern = "_R1_001.fastq.gz", full.names = TRUE)
#Intuition check
head(forward_reads)## [1] "data/01_DADA2/01_raw_gzipped_fastqs/568_4_S245_L001_R1_001.fastq.gz"
## [2] "data/01_DADA2/01_raw_gzipped_fastqs/568_5_S246_L001_R1_001.fastq.gz"
## [3] "data/01_DADA2/01_raw_gzipped_fastqs/581_5_S247_L001_R1_001.fastq.gz"
## [4] "data/01_DADA2/01_raw_gzipped_fastqs/611_5_S248_L001_R1_001.fastq.gz"
## [5] "data/01_DADA2/01_raw_gzipped_fastqs/E03_5_S146_L001_R1_001.fastq.gz"
## [6] "data/01_DADA2/01_raw_gzipped_fastqs/E05_5_S147_L001_R1_001.fastq.gz"
#Create a vector of reverse reads
reverse_reads <-list.files(raw_fastqs_path, pattern = "_R2_001.fastq.gz",
full.names = TRUE)
#Intuition check
head(reverse_reads)## [1] "data/01_DADA2/01_raw_gzipped_fastqs/568_4_S245_L001_R2_001.fastq.gz"
## [2] "data/01_DADA2/01_raw_gzipped_fastqs/568_5_S246_L001_R2_001.fastq.gz"
## [3] "data/01_DADA2/01_raw_gzipped_fastqs/581_5_S247_L001_R2_001.fastq.gz"
## [4] "data/01_DADA2/01_raw_gzipped_fastqs/611_5_S248_L001_R2_001.fastq.gz"
## [5] "data/01_DADA2/01_raw_gzipped_fastqs/E03_5_S146_L001_R2_001.fastq.gz"
## [6] "data/01_DADA2/01_raw_gzipped_fastqs/E05_5_S147_L001_R2_001.fastq.gz"
## [1] FALSE
Let’s see the quality of the raw reads before we trim
# Randomly select 12 samples from dataset to evaluate
# Selecting 12 is typically better than 2 (like we did in class for efficiency)
random_samples <- sample(1:length(reverse_reads), size = 14)
random_samples## [1] 6 1 14 12 11 4 9 10 13 7 2 8 3 5
# Calculate and plot quality of these two samples
forward_filteredQual_plot_14 <- plotQualityProfile(forward_reads[random_samples]) +
labs(title = "Forward Read Raw Quality")
reverse_filteredQual_plot_14 <- plotQualityProfile(reverse_reads[random_samples]) +
labs(title = "Reverse Read Raw Quality")
# Plot them together with patchwork
forward_filteredQual_plot_14 + reverse_filteredQual_plot_14# vector of our samples, extract the sample information from our file
samples <- sapply(strsplit(basename(forward_reads), "_"), function(x)paste(x[1],x[2],sep="_"))
#Intuition check
head(samples)## [1] "568_4" "568_5" "581_5" "611_5" "E03_5" "E05_5"
#place filtered reads into filtered_fastqs_path
filtered_fastqs_path <- "data/01_DADA2/02_filtered_fastqs"
filtered_fastqs_path## [1] "data/01_DADA2/02_filtered_fastqs"
# create 2 variables : filtered_F, filtered_R
filtered_forward_reads <- file.path(filtered_fastqs_path, paste0(samples, "_R1_filtered.fastq.gz"))
#Intuition check
head(filtered_forward_reads)## [1] "data/01_DADA2/02_filtered_fastqs/568_4_R1_filtered.fastq.gz"
## [2] "data/01_DADA2/02_filtered_fastqs/568_5_R1_filtered.fastq.gz"
## [3] "data/01_DADA2/02_filtered_fastqs/581_5_R1_filtered.fastq.gz"
## [4] "data/01_DADA2/02_filtered_fastqs/611_5_R1_filtered.fastq.gz"
## [5] "data/01_DADA2/02_filtered_fastqs/E03_5_R1_filtered.fastq.gz"
## [6] "data/01_DADA2/02_filtered_fastqs/E05_5_R1_filtered.fastq.gz"
## [1] 14
filtered_reverse_reads <- file.path(filtered_fastqs_path, paste0(samples, "_R2_filtered.fastq.gz"))
#Intuition check
head(filtered_reverse_reads)## [1] "data/01_DADA2/02_filtered_fastqs/568_4_R2_filtered.fastq.gz"
## [2] "data/01_DADA2/02_filtered_fastqs/568_5_R2_filtered.fastq.gz"
## [3] "data/01_DADA2/02_filtered_fastqs/581_5_R2_filtered.fastq.gz"
## [4] "data/01_DADA2/02_filtered_fastqs/611_5_R2_filtered.fastq.gz"
## [5] "data/01_DADA2/02_filtered_fastqs/E03_5_R2_filtered.fastq.gz"
## [6] "data/01_DADA2/02_filtered_fastqs/E05_5_R2_filtered.fastq.gz"
## [1] 14
Parameters of filter and trim DEPEND ON THE DATASET
maxN = number of N bases. Remove all Ns from the
data.maxEE = quality filtering threshold applied to expected
errors. By default, all expected errors. Mar recommends using c(1,1).
Here, if there is maxEE expected errors, its okay. If more, throw away
sequence.trimLeft = trim certain number of base pairs on start
of each readtruncQ = truncate reads at the first instance of a
quality score less than or equal to selected number. Chose 2rm.phix = remove phi xcompress = make filtered files .gzippedmultithread = multithread#Assign a vector to filtered reads
#Trim out poor bases, none in this instance.
#Write out filtered fastq files
filtered_reads <-
filterAndTrim(fwd = forward_reads, filt = filtered_forward_reads,
rev = reverse_reads, filt.rev = filtered_reverse_reads,
truncLen = c(245,230), trimLeft = c(9,9),
maxN = 0, maxEE = c(1, 1),truncQ = 2, rm.phix = TRUE,
compress = TRUE, multithread = TRUE)
#These files are described in Kozich et al 2013 AEM
# Describes library prep
#Forward and reverse read have full overlap
# 515F and 806R# Plot the 12 random samples after QC
forward_filteredQual_plot_14 <-
plotQualityProfile(filtered_forward_reads[random_samples]) +
labs(title = "Trimmed Forward Read Quality")
reverse_filteredQual_plot_14 <-
plotQualityProfile(filtered_reverse_reads[random_samples]) +
labs(title = "Trimmed Reverse Read Quality")
# Put the two plots together
forward_filteredQual_plot_14 + reverse_filteredQual_plot_14filterAndTrim## reads.in reads.out
## 568_4_S245_L001_R1_001.fastq.gz 52502 25983
## 568_5_S246_L001_R1_001.fastq.gz 38610 18739
## 581_5_S247_L001_R1_001.fastq.gz 35144 17699
## 611_5_S248_L001_R1_001.fastq.gz 30511 15091
## E03_5_S146_L001_R1_001.fastq.gz 16548 7855
## E05_5_S147_L001_R1_001.fastq.gz 107092 47810
# calculate some stats
filtered_df %>%
reframe(median_reads_in = median(reads.in),
median_reads_out = median(reads.out),
median_percent_retained = (median(reads.out)/median(reads.in)))## median_reads_in median_reads_out median_percent_retained
## 1 45556 21591.5 0.4739551
Note every sequencing run needs to be run
separately! The error model MUST be run separately on
each illumina dataset. If you’d like to combine the datasets from
multiple sequencing runs, you’ll need to do the exact same
filterAndTrim() step AND, very importantly, you’ll
need to have the same primer and ASV length expected by the output.
Infer error rates for all possible transitions within purines and pyrimidines (A<>G or C<>T) and transversions between all purine and pyrimidine combinations.
Error model is learned by alternating estimation of the error rates and inference of sample composition until they converge.
## 75151368 total bases in 318438 reads from 14 samples will be used for learning the error rates.
#Plot forward reads errors
forward_error_plot <-
plotErrors(error_forward_reads, nominalQ = TRUE) +
labs(title = "Forward Read Error Model")
#Reverse reads
error_reverse_reads <-
learnErrors(filtered_reverse_reads, multithread = TRUE)## 70374798 total bases in 318438 reads from 14 samples will be used for learning the error rates.
#Plot reverse reads errors
reverse_error_plot <-
plotErrors(error_reverse_reads, nominalQ = TRUE) +
labs(title = "Reverse Read Error Model")
# Put the two plots together
forward_error_plot + reverse_error_plot## Warning in scale_y_log10(): log-10 transformation introduced infinite values.
## log-10 transformation introduced infinite values.
Both the forward and reverse reads seem to fit the error models pretty well. A2T, C2T, G2T, and T2G seem to have slightly higher errors than expected.
Details of the plot: - Points: The observed error
rates for each consensus quality score.
- Black line: Estimated error rates after convergence
of the machine-learning algorithm.
- Red line: The error rates expected under the nominal
definition of the Q-score.
Similar to what is mentioned in the dada2 tutorial: the estimated error rates (black line) are a “reasonably good” fit to the observed rates (points), and the error rates drop with increased quality as expected. We can now infer ASVs!
An important note: This process occurs separately on forward and reverse reads! This is quite a different approach from how OTUs are identified in Mothur and also from UCHIME, oligotyping, and other OTU, MED, and ASV approaches.
#Infer forward ASVs
dada_forward <- dada(filtered_forward_reads,
err = error_forward_reads, multithread = TRUE)## Sample 1 - 25983 reads in 2046 unique sequences.
## Sample 2 - 18739 reads in 3640 unique sequences.
## Sample 3 - 17699 reads in 3523 unique sequences.
## Sample 4 - 15091 reads in 2923 unique sequences.
## Sample 5 - 7855 reads in 2138 unique sequences.
## Sample 6 - 47810 reads in 9938 unique sequences.
## Sample 7 - 45242 reads in 3730 unique sequences.
## Sample 8 - 28698 reads in 5804 unique sequences.
## Sample 9 - 40995 reads in 6048 unique sequences.
## Sample 10 - 37948 reads in 7660 unique sequences.
## Sample 11 - 7645 reads in 1650 unique sequences.
## Sample 12 - 24444 reads in 6346 unique sequences.
## Sample 13 - 36 reads in 25 unique sequences.
## Sample 14 - 253 reads in 168 unique sequences.
## [1] "list"
#Infer reverse ASVs
dada_reverse <- dada(filtered_reverse_reads,
err = error_reverse_reads, multithread = TRUE)## Sample 1 - 25983 reads in 3883 unique sequences.
## Sample 2 - 18739 reads in 6398 unique sequences.
## Sample 3 - 17699 reads in 6328 unique sequences.
## Sample 4 - 15091 reads in 5140 unique sequences.
## Sample 5 - 7855 reads in 3009 unique sequences.
## Sample 6 - 47810 reads in 14095 unique sequences.
## Sample 7 - 45242 reads in 6052 unique sequences.
## Sample 8 - 28698 reads in 8292 unique sequences.
## Sample 9 - 40995 reads in 9295 unique sequences.
## Sample 10 - 37948 reads in 11519 unique sequences.
## Sample 11 - 7645 reads in 2285 unique sequences.
## Sample 12 - 24444 reads in 8863 unique sequences.
## Sample 13 - 36 reads in 28 unique sequences.
## Sample 14 - 253 reads in 191 unique sequences.
## $`568_4_R2_filtered.fastq.gz`
## dada-class: object describing DADA2 denoising results
## 56 sequence variants were inferred from 3883 input unique sequences.
## Key parameters: OMEGA_A = 1e-40, OMEGA_C = 1e-40, BAND_SIZE = 16
## $Neg1_S148_R2_filtered.fastq.gz
## dada-class: object describing DADA2 denoising results
## 5 sequence variants were inferred from 28 input unique sequences.
## Key parameters: OMEGA_A = 1e-40, OMEGA_C = 1e-40, BAND_SIZE = 16
Now, merge the forward and reverse ASVs into contigs.
# merge forward and reverse ASVs
merged_ASVs <- mergePairs(dada_forward, filtered_forward_reads,
dada_reverse, filtered_reverse_reads,
verbose = TRUE)## 24787 paired-reads (in 52 unique pairings) successfully merged out of 25682 (in 70 pairings) input.
## 17389 paired-reads (in 438 unique pairings) successfully merged out of 17992 (in 529 pairings) input.
## 16071 paired-reads (in 443 unique pairings) successfully merged out of 16906 (in 594 pairings) input.
## 13943 paired-reads (in 324 unique pairings) successfully merged out of 14503 (in 405 pairings) input.
## 7131 paired-reads (in 261 unique pairings) successfully merged out of 7379 (in 318 pairings) input.
## 45847 paired-reads (in 614 unique pairings) successfully merged out of 46839 (in 767 pairings) input.
## 44201 paired-reads (in 63 unique pairings) successfully merged out of 45084 (in 96 pairings) input.
## 27674 paired-reads (in 415 unique pairings) successfully merged out of 28100 (in 481 pairings) input.
## 39686 paired-reads (in 385 unique pairings) successfully merged out of 40343 (in 485 pairings) input.
## 36730 paired-reads (in 526 unique pairings) successfully merged out of 37260 (in 628 pairings) input.
## 7155 paired-reads (in 134 unique pairings) successfully merged out of 7272 (in 163 pairings) input.
## 22883 paired-reads (in 491 unique pairings) successfully merged out of 23642 (in 613 pairings) input.
## 12 paired-reads (in 5 unique pairings) successfully merged out of 12 (in 5 pairings) input.
## 122 paired-reads (in 26 unique pairings) successfully merged out of 140 (in 33 pairings) input.
## [1] "list"
## [1] 14
## [1] "568_4_R1_filtered.fastq.gz" "568_5_R1_filtered.fastq.gz"
## [3] "581_5_R1_filtered.fastq.gz" "611_5_R1_filtered.fastq.gz"
## [5] "E03_5_R1_filtered.fastq.gz" "E05_5_R1_filtered.fastq.gz"
## [7] "E1_4_R1_filtered.fastq.gz" "E1_5_R1_filtered.fastq.gz"
## [9] "E2_4_R1_filtered.fastq.gz" "E2_5_R1_filtered.fastq.gz"
## [11] "E3_4_R1_filtered.fastq.gz" "E3_5_R1_filtered.fastq.gz"
## [13] "Neg1_S148_R1_filtered.fastq.gz" "Neg2_S249_R1_filtered.fastq.gz"
# Create the ASV Count Table
raw_ASV_table <- makeSequenceTable(merged_ASVs)
# Write out the file to data/01_DADA2
# Check the type and dimensions of the data
dim(raw_ASV_table)## [1] 14 1806
## [1] "matrix" "array"
## [1] "integer"
# Inspect the distribution of sequence lengths of all ASVs in dataset
table(nchar(getSequences(raw_ASV_table)))##
## 236 237 291 305
## 1800 3 1 2
# Inspect the distribution of sequence lengths of all ASVs in dataset
# AFTER TRIM
data.frame(Seq_Length = nchar(getSequences(raw_ASV_table))) %>%
ggplot(aes(x = Seq_Length )) +
geom_histogram() +
labs(title = "Raw distribution of ASV length")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
###################################################
###################################################
# TRIM THE ASVS
# Let's trim the ASVs to only be the right size, which is 236.
# 236 originates from our expected amplicon being trimmed due to low quality at the ends
# We will allow for only one length
raw_ASV_table_trimmed <- raw_ASV_table[,nchar(colnames(raw_ASV_table)) %in% 236]
# Inspect the distribution of sequence lengths of all ASVs in dataset
table(nchar(getSequences(raw_ASV_table_trimmed)))##
## 236
## 1800
## [1] 0.9998024
# Inspect the distribution of sequence lengths of all ASVs in dataset
# AFTER TRIM
data.frame(Seq_Length = nchar(getSequences(raw_ASV_table_trimmed))) %>%
ggplot(aes(x = Seq_Length )) +
geom_histogram() +
labs(title = "Trimmed distribution of ASV length")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Sometimes chimeras arise in our workflow.
Chimeric sequences are artificial sequences formed by the combination of two or more distinct biological sequences. These chimeric sequences can arise during the polymerase chain reaction (PCR) amplification step of the 16S rRNA gene, where fragments from different templates can be erroneously joined together.
Chimera removal is an essential step in the analysis of 16S sequencing data to improve the accuracy of downstream analyses, such as taxonomic assignment and diversity assessment. It helps to avoid the inclusion of misleading or spurious sequences that could lead to incorrect biological interpretations.
# Remove the chimeras in the raw ASV table
noChimeras_ASV_table <- removeBimeraDenovo(raw_ASV_table_trimmed,
method="consensus",
multithread=TRUE, verbose=TRUE)## Identified 36 bimeras out of 1800 input sequences.
## [1] 14 1764
## [1] 0.9936489
## [1] 0.9934526
# Plot it
data.frame(Seq_Length_NoChim = nchar(getSequences(noChimeras_ASV_table))) %>%
ggplot(aes(x = Seq_Length_NoChim )) +
geom_histogram()+
labs(title = "Trimmed + Chimera Removal distribution of ASV length")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Here, we will look at the number of reads that were lost in the filtering, denoising, merging, and chimera removal.
# A little function to identify number seqs
getN <- function(x) sum(getUniques(x))
# Make the table to track the seqs
track <- cbind(filtered_reads,
sapply(dada_forward, getN),
sapply(dada_reverse, getN),
sapply(merged_ASVs, getN),
rowSums(noChimeras_ASV_table))
head(track)## reads.in reads.out
## 568_4_S245_L001_R1_001.fastq.gz 52502 25983 25713 25724 24787 24522
## 568_5_S246_L001_R1_001.fastq.gz 38610 18739 18165 18254 17389 17305
## 581_5_S247_L001_R1_001.fastq.gz 35144 17699 17222 17193 16071 15963
## 611_5_S248_L001_R1_001.fastq.gz 30511 15091 14673 14708 13943 13898
## E03_5_S146_L001_R1_001.fastq.gz 16548 7855 7500 7528 7131 7131
## E05_5_S147_L001_R1_001.fastq.gz 107092 47810 47106 47221 45847 45378
# Update column names to be more informative (most are missing at the moment!)
colnames(track) <- c("input", "filtered", "denoisedF", "denoisedR", "merged", "nochim")
rownames(track) <- samples
# Generate a dataframe to track the reads through our DADA2 pipeline
track_counts_df <-
track %>%
# make it a dataframe
as.data.frame() %>%
rownames_to_column(var = "names") %>%
mutate(perc_reads_retained = 100 * nochim / input)
# Visualize it in table format
DT::datatable(track_counts_df)# Plot it!
track_counts_df %>%
pivot_longer(input:nochim, names_to = "read_type", values_to = "num_reads") %>%
mutate(read_type = fct_relevel(read_type,
"input", "filtered", "denoisedF", "denoisedR", "merged", "nochim")) %>%
ggplot(aes(x = read_type, y = num_reads, fill = read_type)) +
geom_line(aes(group = names), color = "grey") +
geom_point(shape = 21, size = 3, alpha = 0.8) +
scale_fill_brewer(palette = "Spectral") +
labs(x = "Filtering Step", y = "Number of Sequences") +
theme_bw()Here, we will use the silva database version 138!
taxa_train <-
assignTaxonomy(noChimeras_ASV_table,
"/workdir/in_class_data/taxonomy/silva_nr99_v138.1_train_set.fa.gz",
multithread=TRUE)
taxa_addSpecies <-
addSpecies(taxa_train,
"/workdir/in_class_data/taxonomy/silva_species_assignment_v138.1.fa.gz")
# Inspect the taxonomy
taxa_print <- taxa_addSpecies # Removing sequence rownames for display only
rownames(taxa_print) <- NULL
#View(taxa_print)Below, we will prepare the following:
ASV_fastas: A fasta file that we can use to build a
tree for phylogenetic analyses (e.g. phylogenetic alpha diversity
metrics or UNIFRAC dissimilarty).########### 2. COUNT TABLE ###############
############## Modify the ASV names and then save a fasta file! ##############
# Give headers more manageable names
# First pull the ASV sequences
asv_seqs <- colnames(noChimeras_ASV_table)
asv_seqs[1:5]## [1] "TGCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGCAGGCGGTCTATTAAGTCAGTGGTGAAAGTTTGCAGCTTAACTGTAAAAGTGCCATTGATACTGGTAGACTTGAGTGTGGTGAAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCATAGATACCACGAAGAACACCGATAGCGAAGGCAGCTTACTGTACCATTACTGACGCTGAGGCACGAAAGCGTGGGGAG"
## [2] "GGCTAGCGTTATCCGGAATTACTGGGCGTAAAGGGTGCGTAGGTGGTTTCTTAAGTCAGAGGTGAAAGGCTACGGCTCAACCGTAGTAAGCCTTTGAAACTGAGAAACTTGAGTGCAGGAGAGGAGAGTAGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAATACCAGTTGCGAAGGCGGCTCTCTGGACTGTAACTGACACTGAGGCACGAAAGCGTGGGGAGC"
## [3] "TGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGTAGGCGGTCTGATAAGTTGGATGTGAAAGCCCCGGGCTCAACCTGGGAACTGCATCCAAAACTGTCAGACTTGAATGCGGAAGAGGGGTGTGGAATTTCCTGTGTAGCGGTGAAATGCGTAGATATAGGAAGGAACATCAGTGGCGAAGGCGACACCCTGGTCTGACATTGACGCTGAGGTACGAAAGCGTGGGGAG"
## [4] "TGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGTAGGCGGCCTTTTAAGTTGGATGTGAAAGCCCCGGGCTTAACCTGGGAACGGCATCCAAAACTGAGAGGCTTGAATGCGGAAGAGGGGTGTGGAATTTCCTGTGTAGCGGTGAAATGCGTAGATATAGGAAGGAACATCAGTGGCGAAGGCGACACCCTGGTCTGACATTGACGCTGAGGTACGAAAGCGTGGGGAG"
## [5] "TGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGTAGGCGGTCTGATAAGTTGGATGTGAAAGCCCCGGGCTCAACCTGGGAACTGCATCCAAAACTGTCAGACTTGAGTGCGGAAGAGGGGTGTGGAATTTCCTGTGTAGCGGTGAAATGCGTAGATATAGGAAGGAACATCAGTGGCGAAGGCGACACCCTGGTCTGACACTGACGCTGAGGTACGAAAGCGTGGGGAG"
# make headers for our ASV seq fasta file, which will be our asv names
asv_headers <- vector(dim(noChimeras_ASV_table)[2], mode = "character")
asv_headers[1:5]## [1] "" "" "" "" ""
# loop through vector and fill it in with ASV names
for (i in 1:dim(noChimeras_ASV_table)[2]) {
asv_headers[i] <- paste(">ASV", i, sep = "_")
}
# intitution check
asv_headers[1:5]## [1] ">ASV_1" ">ASV_2" ">ASV_3" ">ASV_4" ">ASV_5"
# Inspect the taxonomy table
#View(taxa_addSpecies)
##### Prepare tax table
# Add the ASV sequences from the rownames to a column
new_tax_tab <-
taxa_addSpecies%>%
as.data.frame() %>%
rownames_to_column(var = "ASVseqs")
head(new_tax_tab)## ASVseqs
## 1 TGCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGCAGGCGGTCTATTAAGTCAGTGGTGAAAGTTTGCAGCTTAACTGTAAAAGTGCCATTGATACTGGTAGACTTGAGTGTGGTGAAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCATAGATACCACGAAGAACACCGATAGCGAAGGCAGCTTACTGTACCATTACTGACGCTGAGGCACGAAAGCGTGGGGAG
## 2 GGCTAGCGTTATCCGGAATTACTGGGCGTAAAGGGTGCGTAGGTGGTTTCTTAAGTCAGAGGTGAAAGGCTACGGCTCAACCGTAGTAAGCCTTTGAAACTGAGAAACTTGAGTGCAGGAGAGGAGAGTAGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAATACCAGTTGCGAAGGCGGCTCTCTGGACTGTAACTGACACTGAGGCACGAAAGCGTGGGGAGC
## 3 TGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGTAGGCGGTCTGATAAGTTGGATGTGAAAGCCCCGGGCTCAACCTGGGAACTGCATCCAAAACTGTCAGACTTGAATGCGGAAGAGGGGTGTGGAATTTCCTGTGTAGCGGTGAAATGCGTAGATATAGGAAGGAACATCAGTGGCGAAGGCGACACCCTGGTCTGACATTGACGCTGAGGTACGAAAGCGTGGGGAG
## 4 TGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGTAGGCGGCCTTTTAAGTTGGATGTGAAAGCCCCGGGCTTAACCTGGGAACGGCATCCAAAACTGAGAGGCTTGAATGCGGAAGAGGGGTGTGGAATTTCCTGTGTAGCGGTGAAATGCGTAGATATAGGAAGGAACATCAGTGGCGAAGGCGACACCCTGGTCTGACATTGACGCTGAGGTACGAAAGCGTGGGGAG
## 5 TGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGTAGGCGGTCTGATAAGTTGGATGTGAAAGCCCCGGGCTCAACCTGGGAACTGCATCCAAAACTGTCAGACTTGAGTGCGGAAGAGGGGTGTGGAATTTCCTGTGTAGCGGTGAAATGCGTAGATATAGGAAGGAACATCAGTGGCGAAGGCGACACCCTGGTCTGACACTGACGCTGAGGTACGAAAGCGTGGGGAG
## 6 TGCGAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGTAGGCGGCCTTTTAAGTTGGATGTGAAAGCCCCGGGCTCAACCTGGGAACGGCATCCAAAACTGGGAGGCTCGAGTGCGGAAGAGGAGTGTGGAATTTCCTGTGTAGCGGTGAAATGCGTAGATATAGGAAGGAACACCAGTGGCGAAGGCGACACTCTGGTCTGACACTGACGCTGAGGTACGAAAGCGTGGGGAG
## Kingdom Phylum Class
## 1 Bacteria Bacteroidota Bacteroidia
## 2 Bacteria Firmicutes Clostridia
## 3 Bacteria Proteobacteria Gammaproteobacteria
## 4 Bacteria Proteobacteria Gammaproteobacteria
## 5 Bacteria Proteobacteria Gammaproteobacteria
## 6 Bacteria Proteobacteria Gammaproteobacteria
## Order Family Genus
## 1 Cytophagales Cyclobacteriaceae Aureibacter
## 2 Peptostreptococcales-Tissierellales Peptostreptococcaceae Romboutsia
## 3 Pseudomonadales Endozoicomonadaceae Endozoicomonas
## 4 Pseudomonadales Endozoicomonadaceae Endozoicomonas
## 5 Pseudomonadales Endozoicomonadaceae Endozoicomonas
## 6 Pseudomonadales Endozoicomonadaceae Endozoicomonas
## Species
## 1 <NA>
## 2 sedimentorum
## 3 <NA>
## 4 <NA>
## 5 <NA>
## 6 <NA>
# intution check
stopifnot(new_tax_tab$ASVseqs == colnames(noChimeras_ASV_table))
# Now let's add the ASV names
rownames(new_tax_tab) <- rownames(asv_tab)
head(new_tax_tab)## ASVseqs
## ASV_1 TGCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGCAGGCGGTCTATTAAGTCAGTGGTGAAAGTTTGCAGCTTAACTGTAAAAGTGCCATTGATACTGGTAGACTTGAGTGTGGTGAAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCATAGATACCACGAAGAACACCGATAGCGAAGGCAGCTTACTGTACCATTACTGACGCTGAGGCACGAAAGCGTGGGGAG
## ASV_2 GGCTAGCGTTATCCGGAATTACTGGGCGTAAAGGGTGCGTAGGTGGTTTCTTAAGTCAGAGGTGAAAGGCTACGGCTCAACCGTAGTAAGCCTTTGAAACTGAGAAACTTGAGTGCAGGAGAGGAGAGTAGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAATACCAGTTGCGAAGGCGGCTCTCTGGACTGTAACTGACACTGAGGCACGAAAGCGTGGGGAGC
## ASV_3 TGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGTAGGCGGTCTGATAAGTTGGATGTGAAAGCCCCGGGCTCAACCTGGGAACTGCATCCAAAACTGTCAGACTTGAATGCGGAAGAGGGGTGTGGAATTTCCTGTGTAGCGGTGAAATGCGTAGATATAGGAAGGAACATCAGTGGCGAAGGCGACACCCTGGTCTGACATTGACGCTGAGGTACGAAAGCGTGGGGAG
## ASV_4 TGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGTAGGCGGCCTTTTAAGTTGGATGTGAAAGCCCCGGGCTTAACCTGGGAACGGCATCCAAAACTGAGAGGCTTGAATGCGGAAGAGGGGTGTGGAATTTCCTGTGTAGCGGTGAAATGCGTAGATATAGGAAGGAACATCAGTGGCGAAGGCGACACCCTGGTCTGACATTGACGCTGAGGTACGAAAGCGTGGGGAG
## ASV_5 TGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGTAGGCGGTCTGATAAGTTGGATGTGAAAGCCCCGGGCTCAACCTGGGAACTGCATCCAAAACTGTCAGACTTGAGTGCGGAAGAGGGGTGTGGAATTTCCTGTGTAGCGGTGAAATGCGTAGATATAGGAAGGAACATCAGTGGCGAAGGCGACACCCTGGTCTGACACTGACGCTGAGGTACGAAAGCGTGGGGAG
## ASV_6 TGCGAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGTAGGCGGCCTTTTAAGTTGGATGTGAAAGCCCCGGGCTCAACCTGGGAACGGCATCCAAAACTGGGAGGCTCGAGTGCGGAAGAGGAGTGTGGAATTTCCTGTGTAGCGGTGAAATGCGTAGATATAGGAAGGAACACCAGTGGCGAAGGCGACACTCTGGTCTGACACTGACGCTGAGGTACGAAAGCGTGGGGAG
## Kingdom Phylum Class
## ASV_1 Bacteria Bacteroidota Bacteroidia
## ASV_2 Bacteria Firmicutes Clostridia
## ASV_3 Bacteria Proteobacteria Gammaproteobacteria
## ASV_4 Bacteria Proteobacteria Gammaproteobacteria
## ASV_5 Bacteria Proteobacteria Gammaproteobacteria
## ASV_6 Bacteria Proteobacteria Gammaproteobacteria
## Order Family Genus
## ASV_1 Cytophagales Cyclobacteriaceae Aureibacter
## ASV_2 Peptostreptococcales-Tissierellales Peptostreptococcaceae Romboutsia
## ASV_3 Pseudomonadales Endozoicomonadaceae Endozoicomonas
## ASV_4 Pseudomonadales Endozoicomonadaceae Endozoicomonas
## ASV_5 Pseudomonadales Endozoicomonadaceae Endozoicomonas
## ASV_6 Pseudomonadales Endozoicomonadaceae Endozoicomonas
## Species
## ASV_1 <NA>
## ASV_2 sedimentorum
## ASV_3 <NA>
## ASV_4 <NA>
## ASV_5 <NA>
## ASV_6 <NA>
### Final prep of tax table. Add new column with ASV names
asv_tax <-
new_tax_tab %>%
# add rownames from count table for phyloseq handoff
mutate(ASV = rownames(asv_tab)) %>%
# Resort the columns with select
dplyr::select(Kingdom, Phylum, Class, Order, Family, Genus, Species, ASV, ASVseqs)
head(asv_tax)## Kingdom Phylum Class
## ASV_1 Bacteria Bacteroidota Bacteroidia
## ASV_2 Bacteria Firmicutes Clostridia
## ASV_3 Bacteria Proteobacteria Gammaproteobacteria
## ASV_4 Bacteria Proteobacteria Gammaproteobacteria
## ASV_5 Bacteria Proteobacteria Gammaproteobacteria
## ASV_6 Bacteria Proteobacteria Gammaproteobacteria
## Order Family Genus
## ASV_1 Cytophagales Cyclobacteriaceae Aureibacter
## ASV_2 Peptostreptococcales-Tissierellales Peptostreptococcaceae Romboutsia
## ASV_3 Pseudomonadales Endozoicomonadaceae Endozoicomonas
## ASV_4 Pseudomonadales Endozoicomonadaceae Endozoicomonas
## ASV_5 Pseudomonadales Endozoicomonadaceae Endozoicomonas
## ASV_6 Pseudomonadales Endozoicomonadaceae Endozoicomonas
## Species ASV
## ASV_1 <NA> ASV_1
## ASV_2 sedimentorum ASV_2
## ASV_3 <NA> ASV_3
## ASV_4 <NA> ASV_4
## ASV_5 <NA> ASV_5
## ASV_6 <NA> ASV_6
## ASVseqs
## ASV_1 TGCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGCAGGCGGTCTATTAAGTCAGTGGTGAAAGTTTGCAGCTTAACTGTAAAAGTGCCATTGATACTGGTAGACTTGAGTGTGGTGAAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCATAGATACCACGAAGAACACCGATAGCGAAGGCAGCTTACTGTACCATTACTGACGCTGAGGCACGAAAGCGTGGGGAG
## ASV_2 GGCTAGCGTTATCCGGAATTACTGGGCGTAAAGGGTGCGTAGGTGGTTTCTTAAGTCAGAGGTGAAAGGCTACGGCTCAACCGTAGTAAGCCTTTGAAACTGAGAAACTTGAGTGCAGGAGAGGAGAGTAGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAATACCAGTTGCGAAGGCGGCTCTCTGGACTGTAACTGACACTGAGGCACGAAAGCGTGGGGAGC
## ASV_3 TGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGTAGGCGGTCTGATAAGTTGGATGTGAAAGCCCCGGGCTCAACCTGGGAACTGCATCCAAAACTGTCAGACTTGAATGCGGAAGAGGGGTGTGGAATTTCCTGTGTAGCGGTGAAATGCGTAGATATAGGAAGGAACATCAGTGGCGAAGGCGACACCCTGGTCTGACATTGACGCTGAGGTACGAAAGCGTGGGGAG
## ASV_4 TGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGTAGGCGGCCTTTTAAGTTGGATGTGAAAGCCCCGGGCTTAACCTGGGAACGGCATCCAAAACTGAGAGGCTTGAATGCGGAAGAGGGGTGTGGAATTTCCTGTGTAGCGGTGAAATGCGTAGATATAGGAAGGAACATCAGTGGCGAAGGCGACACCCTGGTCTGACATTGACGCTGAGGTACGAAAGCGTGGGGAG
## ASV_5 TGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGTAGGCGGTCTGATAAGTTGGATGTGAAAGCCCCGGGCTCAACCTGGGAACTGCATCCAAAACTGTCAGACTTGAGTGCGGAAGAGGGGTGTGGAATTTCCTGTGTAGCGGTGAAATGCGTAGATATAGGAAGGAACATCAGTGGCGAAGGCGACACCCTGGTCTGACACTGACGCTGAGGTACGAAAGCGTGGGGAG
## ASV_6 TGCGAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGTAGGCGGCCTTTTAAGTTGGATGTGAAAGCCCCGGGCTCAACCTGGGAACGGCATCCAAAACTGGGAGGCTCGAGTGCGGAAGAGGAGTGTGGAATTTCCTGTGTAGCGGTGAAATGCGTAGATATAGGAAGGAACACCAGTGGCGAAGGCGACACTCTGGTCTGACACTGACGCTGAGGTACGAAAGCGTGGGGAG
01_DADA2 filesNow, we will write the files! We will write the following to the
data/01_DADA2/ folder. We will save both as files that
could be submitted as supplements AND as .RData objects for easy loading
into the next steps into R.:
ASV_counts.tsv: ASV count table that has ASV names that
are re-written and shortened headers like ASV_1, ASV_2, etc, which will
match the names in our fasta file below. This will also be saved as
data/01_DADA2/ASV_counts.RData.ASV_counts_withSeqNames.tsv: This is generated with the
data object in this file known as noChimeras_ASV_table. ASV
headers include the entire ASV sequence ~250bps. In addition,
we will save this as a .RData object as
data/01_DADA2/noChimeras_ASV_table.RData as we will use
this data in analysis/02_Taxonomic_Assignment.Rmd to assign
the taxonomy from the sequence headers.ASVs.fasta: A fasta file output of the ASV names from
ASV_counts.tsv and the sequences from the ASVs in
ASV_counts_withSeqNames.tsv. A fasta file that we can use
to build a tree for phylogenetic analyses (e.g. phylogenetic alpha
diversity metrics or UNIFRAC dissimilarty).ASVs.fasta in
data/02_TaxAss_FreshTrain/ to be used for the taxonomy
classification in the next step in the workflow.track_read_counts.RData: To track how many reads we
lost throughout our workflow that could be used and plotted later. We
will add this to the metadata in
analysis/02_Taxonomic_Assignment.Rmd.# FIRST, we will save our output as regular files, which will be useful later on.
# Save to regular .tsv file
# Write BOTH the modified and unmodified ASV tables to a file!
# Write count table with ASV numbered names (e.g. ASV_1, ASV_2, etc)
write.table(asv_tab, "data/01_DADA2/ASV_counts.tsv", sep = "\t", quote = FALSE, col.names = NA)
# Write count table with ASV sequence names
write.table(noChimeras_ASV_table, "data/01_DADA2/ASV_counts_withSeqNames.tsv", sep = "\t", quote = FALSE, col.names = NA)
# Write out the fasta file for reference later on for what seq matches what ASV
asv_fasta <- c(rbind(asv_headers, asv_seqs))
# Save to a file!
write(asv_fasta, "data/01_DADA2/ASVs.fasta")
# SECOND, let's save the taxonomy tables
# Write the table
write.table(asv_tax, "data/01_DADA2/ASV_taxonomy.tsv", sep = "\t", quote = FALSE, col.names = NA)
# THIRD, let's save to a RData object
# Each of these files will be used in the analysis/02_Taxonomic_Assignment
# RData objects are for easy loading :)
save(noChimeras_ASV_table, file = "data/01_DADA2/noChimeras_ASV_table.RData")
save(asv_tab, file = "data/01_DADA2/ASV_counts.RData")
# And save the track_counts_df a R object, which we will merge with metadata information in the next step of the analysis in nalysis/02_Taxonomic_Assignment.
save(track_counts_df, file = "data/01_DADA2/track_read_counts.RData")##Session information
## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 4.3.2 (2023-10-31)
## os Rocky Linux 9.0 (Blue Onyx)
## system x86_64, linux-gnu
## ui X11
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz America/New_York
## date 2024-03-14
## pandoc 3.1.1 @ /usr/lib/rstudio-server/bin/quarto/bin/tools/ (via rmarkdown)
##
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## [1] /home/cab565/R/x86_64-pc-linux-gnu-library/4.3
## [2] /programs/R-4.3.2/library
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